Feature Engineering for Multimodal Data: From Laptop to Cluster with LanceDB

LanceDB’s Geneva feature-engineering package is presented as a table-centric alternative to custom multimodal data scripts that generate separate metadata, embeddings, and sidecar files. Users define Python UDFs to derive features from table columns, attach those functions as new columns, and use backfills that handle batching, parallel execution, checkpoints, retries, and incremental updates while keeping raw media, derived attributes, and vector-search indexes together in a versioned LanceDB table. The geneva-examples repository demonstrates local and enterprise workflows for image, video, and PDF pipelines, including image ingestion, metadata extraction, CLIP embeddings, BLIP captions, and text-to-image search. The underlying workflow relies on a small set of API calls for database connection, table creation, UDF definition, column addition, backfilling, and search, with the same UDF code able to run locally or on managed GPU infrastructure. The post also describes model-backed batched UDFs, resource declarations, dependency manifests for remote workers, error handling, column recomputation for updated models, and operational capabilities such as job tracking, resumability, and independent scaling of storage and compute.

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